The Sequence Opinion #754: Generalist vs. Specialist: Which School Will Win in Mathematical AI
A pragmatic view of some of the biggest quesitons about AI in mathematics.
Lets discuss one of the most important debate in AI for math: do we need specialists models or the general multimodal foundation models will inevitably win?
Artificial intelligence has begun to make significant inroads into the field of mathematics. By 2025, large “foundation” AI models are tackling tasks from high school algebra homework all the way up to difficult research conjectures. Broadly, these models fall into two categories: generalist models (like OpenAI’s GPT-4, Google’s Gemini, or Anthropic’s Claude), which are trained on a wide array of information and can handle many types of questions, and specialized math models (such as Google’s Minerva, DeepMind’s AlphaCode and AlphaProof, or Microsoft’s MathPrompter technique), which focus specifically on mathematical reasoning. Each type has different strengths. Generalist models have shown a surprising ability to solve standard math problems and even achieve high scores on competitive exams, yet they often falter when asked to produce truly novel mathematical insights. Specialized models, in contrast, can deliver more rigorous or precise results in their niche, but they come with limitations regarding scope, credibility, and flexibility. In this essay, we compare these generalist and specialist AI systems in mathematics – examining how they perform on benchmarks like the International Mathematical Olympiad (IMO), why original research remains challenging for them, what concrete tasks they are being used for, and how the leading models stack up in terms of methodology, strengths, and weaknesses.